Biometric Security Systems (BSS) are increasingly becoming popular due to the ease of signal collection through wearable& Biometrics such as electroencephalogram (EEG) contain information that are unique to a person, nearly impossible to impersonate without invading personal space, and chaotic over time, that makes them favorable for security applications. For example, several researchers have proposed EEG-based Security Systems (ESS) for authentication of credentials. The core assumption in many BSS including ESS is that the biometric signal is chaotic, and has high entropy, that cannot be guessed by an adversary, potentially blocking spoofing attack. As entropy increases the effort required by the adversary to guess the signal also increases. In this sense, entropy measures are common evaluation metrics of security strength.
It is with these observations in mind, among others, that various aspects of the present disclosure were conceived and developed.
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